code: def test_incremental_text_pipeline(container):
      X = pd.Series(["a list", "of words", "for classification"] * 100)
      X = dd.from_pandas(X, npartitions=3)
  
      if container == "bag":
          X = X.to_bag()
  
      y = da.from_array(np.array([0, 0, 1] * 100), chunks=(100,) * 3)
  
      assert tuple(X.map_partitions(len).compute()) == y.chunks[0]
  
      sgd = SGDClassifier(max_iter=5, tol=1e-3)
      clf = Incremental(
          sgd, scoring="accuracy", assume_equal_chunks=True
      )
      vect = dask_ml.feature_extraction.text.HashingVectorizer()
      pipe = make_pipeline(vect, clf)
  
      pipe.fit(X, y, incremental__classes=[0, 1])
      X2 = pipe.steps[0][1].transform(X)
      assert hasattr(clf, "coef_")
  
      X2.compute_chunk_sizes()
      assert X2.shape == (300, vect.n_features)
  
      preds = pipe.predict(X).compute()
      assert len(y) == len(preds)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from dask.array.utils import assert_eq
  from scipy.sparse import csr_matrix
  from sklearn.base import clone
  from sklearn.linear_model import SGDClassifier, SGDRegressor
  from sklearn.pipeline import make_pipeline
  import dask_ml.feature_extraction.text
  import dask_ml.metrics
  from dask_ml.metrics.scorer import check_scoring
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 11 for calling a global function with example usage: test_incremental_text_pipeline(container) without return types